Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Containing Queensland prickly pear : buffer zones, closer settlement, whiteness

By 1925, the introduced prickly pear (Opuntia and Nopalea spp.) covered up to 60 million acres of Queensland and New South Wales in what was perceived as prime agricultural land. After 40 years of experimentation, all Queensland Government strategies had failed. Faced with this failure and a diminishing expectation that the land would ever be conquered, buffer zones were proposed by the newly formed Queensland Prickly Pear Land Commission. A close reading of government documents, newspaper reports and local histories about these buffer zones shows how settler anxieties over who could or should occupy the land shaped the kinds of strategies recommended and adopted in relation to this alien species. Physical and cultural techniques were used to manage the uneasy coexistence between prickly pear, on the one hand, and farmers and graziers on the other. Furthermore, this environmental history challenges the notion of racially homogenous closer settlement under the White Australia Policy, showing the many different kinds of livelihood and labour in prickly pear land in the 1920s.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Fasting increases serum bilirubin levels in clinically normal, healthy males but not females : a retrospective study from phase I clinical trial participants

Aim: To examine if fasting affects serum bilirubin levels in clinical healthy males and females. Methods: We utilised retrospective data from phase 1 clinical trials where blood was collected in either a fed or fasting state at screening and pre-dosing time points and analysed for total bilirubin levels as per standard clinical procedures. Participants were clinically healthy males (n = 105) or females (n = 30) aged 18 to 48 inclusive who participated in a phase 1 clinical trial in 2012 or 2013. Results: We found a statistically significant increase in total serum bilirubin levels in fasting males as compared to non-fasting males. The fasting time correlated positively with increased bilirubin levels. The age of the healthy males did not correlate with their fasting bilirubin level. We found no correlation between fasting and bilirubin levels in clinically normal females. Conclusions: The recruitment and screening of volunteers for a clinical trial is a time-consuming and expensive process. This study clearly demonstrates that testing for serum bilirubin should be conducted on non-fasting male subjects. If fasting is required, then participants should not be excluded from a trial based on an elevated serum bilirubin that is deemed non-clinically significant.

Acoustic emission condition monitoring : an application for wind turbine fault detection

Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.

Normal human mammary epithelial cells spontaneously escape senescence and acquire genomic changes

Senescence and genomic integrity are thought to be important barriers in the development of malignant lesions. Human fibroblasts undergo a limited number of cell divisions before entering an irreversible arrest, called senescence. Here we show that human mammary epithelial cells (HMECs) do not conform to this paradigm of senescence. In contrast to fibroblasts, HMECs exhibit an initial growth phase that is followed by a transient growth plateau (termed selection or M0; refs 3-5), from which proliferative cells emerge to undergo further population doublings (approximately 20-70), before entering a second growth plateau (previously termed senescence or M1; refs 4-6). We find that the first growth plateau exhibits characteristics of senescence but is not an insurmountable barrier to further growth. HMECs emerge from senescence, exhibit eroding telomeric sequences and ultimately enter telomere-based crisis to generate the types of chromosomal abnormalities seen in the earliest lesions of breast cancer. Growth past senescent barriers may be a pivotal event in the earliest steps of carcinogenesis, providing many genetic changes that predicate oncogenic evolution. The differences between epithelial cells and fibroblasts provide new insights into the mechanistic basis of neoplastic transformation.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Collagen-induced activation of the Mr. 72,000 type IV collagenase in normal and malignant human fibroblastoid cells

Although the Mr. 72,000 type IV collagenase (matrix metalloproteinase 2) has been implicated in a variety of normal and pathogenic processes, its activation mechanism in vivo is unclear. We have found that fibroblasts from normal and neoplastic human breast, as well as the sarcomatous human Hs578T and HT1080 cell lines, activate endogenous matrix metalloprotease 2 when cultured on type I collagen gels, but not on plastic, fibronectin, collagen IV, gelatin, matrigel, or basement membrane-like HR9 cell matrix. This activation is monitored by the zymographic detection of Mr 59,000 and/or Mr 62,000 species, requires 2-3 days of culture on vitrogen to manifest, is cycloheximide inhibitable, and correlates with an arborized morphology. A similar activation pattern was seen in these cells in response to Concanavalin A but not transforming growth factor β or 12-O-tetradecanoylphorbol-13-acetate. The interstitial matrix may thus play an important role in regulating matrix degradation in vivo.

Sensor fault-resilient control of interior permanent-magnet synchronous motor drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Epithelial-mesenchymal interconversions in normal ovarian surface epithelium and ovarian carcinomas : an exception to the norm

Cancer that arises from the ovarian surface epithelium (OSE) accounts for approximately 90% of human ovarian cancer, and is the fourth leading cause of cancer-related deaths among women in developed countries. The pathophysiology of epithelial ovarian cancer is still unclear because of the poor understanding of the complex nature of its development and the unusual mechanism(s) of disease progression. Recent studies have reported epithelial-mesenchymal transition (EMT) in cultured OSE and ovarian cancer cell lines in response to various stimuli, but our understanding of the importance of these observations for normal ovarian physiology and cancer progression is not well established. This review highlights the current literature on EMT-associated events in normal OSE and ovarian cancer cell lines, and discusses its implication for normal ovarian function as well as acquisition of neoplastic phenotypes. The pathological changes in OSE in response to EMT during neoplastic transformation and the contribution of hormones, growth factors, and cytokines that initiate and drive EMT to sustain normal ovarian function, as well as cancer development and progression are also discussed. Finally, emphasis is placed on the clinical implications of EMT and potential therapeutic opportunities that may arise from these observations have been proposed.